Name: R for Data Science
ISBN: 9781491910399

R for Data Science

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle-transform your datasets into a form convenient for analysis Program-learn powerful R tools for solving data problems with greater clarity and ease Explore-examine your data, generate hypotheses, and quickly test them Model-provide a low-dimensional summary that captures true "signals" in your dataset Communicate-learn R Markdown for integrating prose, code, and results

What Hacker News says

tylermwJun 2024

My personal favorite resource is "R for Data Science" by Hadley Wickham. It covers lots of nice data manipulation and visualization examples, and provides a good introduction to the tidyverse, which is a particular dialect of R that's well-suited for data analysis. It's available for free at:

https://r4ds.hadley.nz/

For more specialized analytical methods there are lots of textbooks out there that provide a deep dive into packages for a specific field (e.g. survival analysis, machine learning, time series), but for general data manipulation and visualization it's hard to beat R4DS.

thread

landosaariMar 2024

Susan Rigetti has a nice overview [0]

In this Susan recommends https://www.khanacademy.org/, which is good for most things learned in primary or secondary school.

There is an open source curriculum equivalent for an undergrad compromised of MOOC and similar resources [1]

Similar to the above is a markdown [2], with a scope directed towards people coming from a CS background.

After this perhaps focus more on statistics & data science for metric interpretation. R for Data Science [3] is an option.

[0] https://www.susanrigetti.com/math

[1] https://github.com/ossu/math

[2] https://github.com/NavigoLearn/RoadmapsMarkdown/blob/master/... for computer science/full-roadmap.md

[3] https://r4ds.hadley.nz/

thread

acomjeanFeb 2023

Rstudio is a pretty nice user interface to the R language. datasets can be browsed like spreadsheet tables. ggplot2 is a great graphing tool. Used in science a lot by non programmers.

there is an online book too thats pretty decent: R for data science

https://r4ds.had.co.nz/

thread

sargstuffSep 2022

Plenty of visual tropes from various software example galleries.

If weren't for the 2d/3d limitation, would have gone with https://flowingdata.com/2019/10/08/visualization-gifs-imagem... instead of the stata-mation stuff.

* Chart types :

   * latex-cookbook.net : https://latex-cookbook.net/

   * ggplot2 gallery :  https://exts.ggplot2.tidyverse.org/gallery/  https://plotnine.readthedocs.io/en/stable/gallery.html
  
   * "ggplot2: Elegant graphics for data analysis" : https://ggplot2-book.org/

   * Rstudio gallery : https://community.rstudio.com/c/table-gallery/64

   * https://flowingdata.com/chart-types/ && https://flowingdata.com/category/tutorials/

   * https://reference.wolfram.com/language/guide/StatisticalVisualization.html

** xkcd like stuff:

  ** food for thought : https://flowingdata.com/2016/12/12/calories-in-fast-food-menu-items/

  ** heat map in excell : https://flowingdata.com/2020/09/29/how-to-make-a-heatmap-in-excel/

  ** juggling the statistics : https://flowingdata.com/2015/12/15/a-day-in-the-life-of-americans/

  ** https://flowingdata.com/category/projects/data-underload/

** xkcd NLP approach :

  ** https://flowingdata.com/famous-movie-quotes-as-charts/

  ** https://flowingdata.com/2010/05/26/famous-science-fiction-quotes-in-graph-form/

  ** http://www.visguy.com/2010/05/27/sci-fi-movie-quotes-visualized/

** General composibility techniques with examples:

   ** "The Grammar of Graphics" by Leland Wilkinson

   ** R for Data Science : https://r4ds.had.co.nz/data-visualisation.html

   ** "Visualize This: The FlowingData Guide to Design, Visualization, and Statistics" : https://book.flowingdata.com/

   ** Flowing data : https://flowingdata.com/2020/09/08/telling-stories-in-visual-data-driven-essays/

thread

PeritractJul 2022

R for Data Science [1] is well-worth reading. Even if you're not planning on writing much R in the future, the book is a solid introduction to principles & practices, starting from an accessible level.

I'd also argue that it's beneficial to have a decent understanding of R when working with data, no matter what tool you favour. It's widely used, and so interesting things are written with it, and you're cutting yourself off from knowledge if you don't have at least a basic understanding.

[1] https://r4ds.had.co.nz/

thread

i_am_proteusMay 2022

Those interested in R with tidyverse may find "R for Data Science" (https://r4ds.had.co.nz) to be a valuable resource.

thread